학술논문

ConFusion: Sensor Fusion for Complex Robotic Systems Using Nonlinear Optimization
Document Type
Periodical
Source
IEEE Robotics and Automation Letters IEEE Robot. Autom. Lett. Robotics and Automation Letters, IEEE. 4(2):1093-1100 Apr, 2019
Subject
Robotics and Control Systems
Computing and Processing
Components, Circuits, Devices and Systems
Robot sensing systems
Sensor fusion
Optimization
Measurement uncertainty
Time measurement
Kalman filters
mobile manipulation
Language
ISSN
2377-3766
2377-3774
Abstract
We present ConFusion, an open-source package for online sensor fusion for robotic applications. ConFusion is a modular framework for fusing measurements from many heterogeneous sensors within a moving horizon estimator. ConFusion offers greater flexibility in sensor fusion problem design than filtering-based systems and the ability to scale the online estimate quality with the available computing power. We demonstrate its performance in comparison to an iterated extended Kalman filter in visual-inertial tracking, and show its versatility through whole-body sensor fusion on a mobile manipulator.